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Warehouse Reduction

In this use case, we will focus on Warehouse Reduction, where the objective is to reduce the number of warehouses from 6 to 5. This kind of scenario typically occurs when a company aims to optimize costs by closing one or more underperforming warehouses while still maintaining efficient service levels for customers.

Problem Statement

We currently have a network of 6 warehouses serving a large customer base. The goal is to reduce this to 5 warehouses while ensuring that:

  • Service Levels: The majority of customers must still be served within the required service distance.
  • Cost Efficiency: The overall operational and transportation costs should decrease, even after removing one warehouse.
  • Reassigning Customers: Some customers will need to be reassigned to other warehouses, and the network must be rebalanced accordingly.
The map shows the 100 customers (blue dots) and the 6 existing warehouses (orange markers) currently serving them.

In this scenario, the challenge is to identify which warehouse to close and how to redistribute the customer assignments effectively to the remaining 5 warehouses, while maintaining optimal service efficiency.

Preparing Data

We will now prepare the input data to solve the problem of reducing the number of warehouses from 6 to 5. You can download the input data file here.

Parameters Sheet:

  • Minimum Number of Sites and Maximum Number of Sites: Set both to 5, as we aim to reduce the number of warehouses to exactly 5.
  • Minimum Candidate Sites and Maximum Candidate Sites: Set both to 5, ensuring that all the final warehouses are chosen from the existing ones listed in the Sites sheet.

Other parameters, such as warehouse capacities and fixed costs, can be adjusted based on your specific business requirements.

Customers Sheet:

This sheet provides detailed information about all the customers, each with varying demand levels. The key columns include:

  • Customer ID: A unique identifier for each customer.
  • Customer Address: The location of the customer.
  • Latitude and Longitude: Geographical coordinates, which will be used to calculate distances between customers and potential warehouse sites.
  • Customer Demand: The total demand from each customer, which must be met by one or more warehouses.

Sites Sheet:

In the Sites sheet:

  • Include information on all the existing 6 warehouses.
  • Set the Mandatory column to FALSE for all the warehouses, allowing the tool to decide which one should be closed.

This data setup will allow the network design tool to analyze and optimize which warehouse to remove while maintaining efficient service levels and minimizing costs.

Establishing a Base Case

Before proceeding with the warehouse reduction, it is essential to establish a base case—the best result achievable with the current set of 6 warehouses. This will serve as a benchmark to compare against the results after the warehouse reduction.

Modifying the Input Parameters

To create the base case, make a copy of the input file prepared in the previous step, and modify the file as follows:

  • Minimum Number of Sites: Set to 6.
  • Maximum Number of Sites: Set to 6.
  • Minimum Candidate Sites: Set to 6.
  • Maximum Candidate Sites: Set to 6.

By setting these values to 6, the model will be forced to consider all 6 warehouses and optimize within this configuration. All other parameters, such as customer demand and warehouse capacity, remain the same as in the previous setup.

This base case will provide insights into the current performance of the warehouse network, allowing for an effective comparison when reducing the number of warehouses in subsequent steps.

Using the Network Design Tool

To establish the base case and proceed with warehouse reduction, follow these steps in Convect AI’s Flow Platform:

  1. Login to Convect AI's Flow Platform Navigate to https://flow.convect.ai. Log in with your credentials.

  2. Open the Network Design App Once logged in, locate the Network Design app and open it.

  3. Create a New File Click the "Create File" button to start a new project.

  4. Upload the Baseline Input Data File On the file creation screen, provide the required project information and upload the baseline input data file (with the parameters set for 6 warehouses). Wait for the data import process to complete.

  5. Solve the Problem Once the data is successfully imported, click the Solve button to run the optimization. Wait until the process is complete.

  6. View the Results After the run is completed, click the Output View tab to review the results in a spreadsheet format. To visualize the customer assignments and other metrics, go to the Graph View tab for built-in visualizations.

Repeat the Steps for the Warehouse Reduction Scenario

Repeat steps 3 to 6 for the warehouse reduction scenario, using the previously prepared input file where the number of sites is set to 5. This will allow you to compare the results of the base case and the reduced network, providing insights into the cost and efficiency changes.

Comparing Results

To compare the results from the 6-warehouse and 5-warehouse scenarios, follow these steps in Convect AI’s Flow Platform:

  1. Go to the Workbench Page In the Network Design app, navigate to the Workbench page where you can view previous runs.

  2. Select the Files for Comparison Click on the two files corresponding to the 6-warehouse and 5-warehouse cases to select them for comparison.

  3. Open the Compare Option Click the three-dot icon to the right of the Create File button. This opens a dropdown menu where you can select Compare.

  4. Configure Comparison In the popup box, select Output View as the view type and choose the Scenario Summary table. Click Confirm.

  5. Compare the Results The page that follows will display the comparison of key metrics from both scenarios, side by side, enabling you to analyze the differences between the 6-warehouse and 5-warehouse cases.

Analyzing Results

1. Total Cost Comparison

  • 6-Warehouse Case: The total cost for the current network configuration with 6 warehouses is $85.7M. This cost includes both warehouse operational costs and transportation costs for delivering goods to customers.

  • 5-Warehouse Case: Reducing the number of warehouses to 5 results in a lower total cost of $83.3M. The cost savings come from closing one warehouse and redistributing customers to the remaining warehouses, which in this case outweighs any increase in transportation costs.

2. Warehouse Closure

In the Graph View of the 5-warehouse scenario, the optimization algorithm suggests closing the warehouse located near Ciudad Juarez. The tool identifies this warehouse as the least cost-efficient in terms of balancing operational costs against transportation cost savings.

The map displays the 5 warehouses selected to remain operational after the reduction from 6. The warehouse near Ciudad Juarez has been excluded from the network, and customers previously assigned to that warehouse are now served by the remaining locations.

3. Customer Assignments

  • In the 6-Warehouse Case, customers are distributed across all 6 warehouses, with each warehouse handling its assigned set of customers.

  • In the 5-Warehouse Case, the customers previously served by the warehouse near Ciudad Juarez are reassigned to other warehouses.

Conclusion

By comparing the results, we observe that reducing the number of warehouses from 6 to 5 leads to a cost reduction from $85.7M to $83.3M. This suggests that, in some cases, closing a warehouse can indeed reduce overall costs, particularly when the operational cost of a warehouse exceeds the transportation cost savings it provides.

This warehouse reduction strategy can be an effective way to optimize network efficiency, especially when operational costs of maintaining multiple warehouses outweigh their logistical benefits.